{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plan\n",
    "\n",
    "- 4.24\n",
    "\n",
    "look for material/code\n",
    "\n",
    "write weekly report\n",
    "\n",
    "Lynn: transformer \n",
    "\n",
    "Miko: log reg\n",
    "\n",
    "Qiling: LSTM\n",
    "\n",
    "- 4.26\n",
    "\n",
    "label2 (kmeans，human annotation)\n",
    "\n",
    "- 4.27-5.2\n",
    "\n",
    "3 models implementation (LSTM, log reg, transformer)\n",
    "\n",
    "make table visualization\n",
    "\n",
    "- 5.2-5.4\n",
    "\n",
    "final report\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset Overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>cat</th>\n",
       "      <th>topic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>SeaWorld’s profits fell by 84% and customers a...</td>\n",
       "      <td>ele</td>\n",
       "      <td>SeaWorld</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>The company teaches dolphins and killer whales...</td>\n",
       "      <td>ele</td>\n",
       "      <td>SeaWorld</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>It says fewer people are going to its parks an...</td>\n",
       "      <td>ele</td>\n",
       "      <td>SeaWorld</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>SeaWorld has been in the news since the 2013 d...</td>\n",
       "      <td>ele</td>\n",
       "      <td>SeaWorld</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Animal rights organizations say that orcas kep...</td>\n",
       "      <td>ele</td>\n",
       "      <td>SeaWorld</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  cat     topic\n",
       "0  SeaWorld’s profits fell by 84% and customers a...  ele  SeaWorld\n",
       "1  The company teaches dolphins and killer whales...  ele  SeaWorld\n",
       "2  It says fewer people are going to its parks an...  ele  SeaWorld\n",
       "3  SeaWorld has been in the news since the 2013 d...  ele  SeaWorld\n",
       "4  Animal rights organizations say that orcas kep...  ele  SeaWorld"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('dataset.csv', encoding = 'utf-8')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7395"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are missing values in train data: False\n",
      "min len in df:  12\n",
      "max len in df:  1991\n"
     ]
    }
   ],
   "source": [
    "# check missing data\n",
    "print('There are missing values in train data:', df.isnull().values.any())\n",
    "\n",
    "# more info\n",
    "print('min len in df: ', len(min(df['text'], key=len)))\n",
    "print('max len in df: ', len(max(df['text'], key=len)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>topic</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cat</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>adv</td>\n",
       "      <td>2650</td>\n",
       "      <td>2650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>ele</td>\n",
       "      <td>2150</td>\n",
       "      <td>2150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>int</td>\n",
       "      <td>2595</td>\n",
       "      <td>2595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     text  topic\n",
       "cat             \n",
       "adv  2650   2650\n",
       "ele  2150   2150\n",
       "int  2595   2595"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('cat').count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Text Pre-Processing\n",
    "\n",
    "Currently only made process of label `topic`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Error loading stopwords: <urlopen error [Errno 61]\n",
      "[nltk_data]     Connection refused>\n",
      "[nltk_data] Error loading wordnet: <urlopen error [Errno 61]\n",
      "[nltk_data]     Connection refused>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Text pre-processing\n",
    "\"\"\"removes punctuation, stopwords, and returns a list of the remaining words, or tokens\"\"\"\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Cleaning the text\n",
    "import string\n",
    "def text_process(text):\n",
    "    '''\n",
    "    Takes in a string of text, then performs the following:\n",
    "    1. Remove all punctuation\n",
    "    2. Remove all stopwords\n",
    "    3. Return the cleaned text as a list of words\n",
    "    4. Remove words\n",
    "    '''\n",
    "    stemmer = WordNetLemmatizer()\n",
    "    nopunc = [char for char in text if char not in string.punctuation]\n",
    "    nopunc = ''.join([i for i in nopunc if not i.isdigit()])\n",
    "    nopunc =  [word.lower() for word in nopunc.split() if word.lower() not in stopwords.words('english')]\n",
    "    return ' '.join([stemmer.lemmatize(word) for word in nopunc])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "# topic list\n",
    "topic = []\n",
    "for t in df.topic:\n",
    "    mytopic = text_process(t)\n",
    "    if (mytopic not in topic):\n",
    "        topic.append(mytopic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tokenization and Splitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 272 unique tokens.\n"
     ]
    }
   ],
   "source": [
    "# tokenization\n",
    "max_words = 2000\n",
    "#tokenizer = Tokenizer(num_words=max_words)\n",
    "tokenizer = Tokenizer()\n",
    "tokenizer.fit_on_texts(topic)\n",
    "\n",
    "word_index = tokenizer.word_index\n",
    "vocab_size = len(word_index)+1\n",
    "print('Found %s unique tokens.' % len(word_index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "# integer encode the documents\n",
    "encoded_topic = tokenizer.texts_to_sequences(topic)\n",
    "\n",
    "# pad documents to a max length of 4 words (no used)\n",
    "#max_length = 4\n",
    "#padded_topic = pad_sequences(encoded_topic, maxlen=max_length, padding='post')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GloVe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import spatial # embeddings\n",
    "from sklearn.manifold import TSNE # visualization\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Flatten, Embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 400000 word vectors.\n"
     ]
    }
   ],
   "source": [
    "# create embedding_dict (takes long time)\n",
    "embeddings_dict = {}\n",
    "path = ' ' # modify\n",
    "with open(path + \"/glove.6B.50d.txt\", 'r', encoding=\"utf-8\") as f:\n",
    "    for line in f:\n",
    "        values = line.split()\n",
    "        token = values[0]\n",
    "        vector = np.asarray(values[1:], \"float32\")\n",
    "        embeddings_dict[token] = vector\n",
    "\n",
    "print('Found %s word vectors.' % len(embeddings_dict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_closest_embeddings(embedding, cutoff=25):\n",
    "    return sorted(embeddings_dict.keys(), key=lambda token: spatial.distance.euclidean(embeddings_dict[token], embedding))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['fingernails', 'toenails', 'stringy', 'peeling', 'shove']\n"
     ]
    }
   ],
   "source": [
    "# check\n",
    "print(find_closest_embeddings(embeddings_dict[\"twig\"] - embeddings_dict[\"branch\"] + embeddings_dict[\"hand\"])[:5])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Distance between \"door\" and \"window\" is 2.47\n",
      "Distance between \"door\" and \"doos\" is 8.63\n"
     ]
    }
   ],
   "source": [
    "# check\n",
    "a = embeddings_dict['door']\n",
    "b = embeddings_dict['window']\n",
    "c = embeddings_dict['doos']\n",
    "dist1 = np.linalg.norm(a-b)\n",
    "dist2 = np.linalg.norm(a-c)\n",
    "print('Distance between \"door\" and \"window\" is', \"%.2f\"%(dist1))\n",
    "print('Distance between \"door\" and \"doos\" is', \"%.2f\"%(dist2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.142    ,  0.0073347, -0.34446  ,  1.5933   , -0.091562 ,\n",
       "       -1.0505   , -1.7131   , -1.1196   ,  1.4542   ,  0.69535  ,\n",
       "        0.020637 ,  0.42931  ,  0.13775  , -0.14925  ,  0.80284  ,\n",
       "       -0.57233  , -0.34445  ,  0.64497  , -1.2866   , -0.44647  ,\n",
       "       -0.67966  ,  0.2884   , -0.89558  , -0.12658  , -0.15142  ,\n",
       "        0.35438  ,  1.1058   , -0.030558 , -0.010526 , -0.92145  ,\n",
       "       -1.0597   ,  0.23135  , -0.37844  , -1.1166   , -0.090823 ,\n",
       "       -0.45357  ,  0.036662 , -0.71701  ,  0.049564 ,  0.5766   ,\n",
       "       -1.0299   , -0.10107  , -0.31862  ,  0.85357  ,  0.57089  ,\n",
       "       -0.24428  , -0.39856  , -1.2157   ,  0.19371  ,  0.54571  ],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check\n",
    "embeddings_dict['seaworld']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "phrase_embeddings_list = []\n",
    "\n",
    "for phrase_index in encoded_topic:\n",
    "    if len(phrase_index) != 0:\n",
    "        length = len(phrase_index)\n",
    "    embedding_vector = 0\n",
    "    for i in phrase_index:\n",
    "        embedding_vector += embeddings_dict[list(word_index)[i-1]]\n",
    "    embedding_vector = embedding_vector/length\n",
    "    phrase_embeddings_list.append(embedding_vector)\n",
    "\n",
    "# convert list to numpy matrix\n",
    "embeddings_matrix = np.zeros((len(topic), 50))\n",
    "\n",
    "for i in range(0,len(phrase_embeddings_list)):\n",
    "    embeddings_matrix[i] = phrase_embeddings_list[i] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.14199996,  0.0073347 , -0.34446001, ..., -1.21570003,\n",
       "         0.19371   ,  0.54571003],\n",
       "       [ 0.26949999, -0.30676001, -1.1681    , ...,  0.39972001,\n",
       "        -0.19351   ,  0.94417   ],\n",
       "       [-0.011035  ,  1.70159996,  0.22262999, ..., -0.58366001,\n",
       "         0.74236   , -0.98032999],\n",
       "       ...,\n",
       "       [ 0.84802997, -0.21131   , -0.78253001, ..., -0.4461    ,\n",
       "        -0.064616  ,  0.16507   ],\n",
       "       [ 0.289473  ,  0.48859999, -0.71358001, ...,  0.302935  ,\n",
       "        -0.74865496, -0.20778   ],\n",
       "       [ 0.54351997, -0.45590001,  0.40635002, ...,  0.164205  ,\n",
       "         0.33906999,  0.02444999]])"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# embedding_matrix visualization\n",
    "tsne = TSNE(n_components=2, random_state=0)\n",
    "# words =  list(embeddings_dict.keys()) # for all 40000 vectors\n",
    "# vectors = [embeddings_dict[word] for word in words]\n",
    "words = [word for word in topic]\n",
    "vectors = [embeddings_matrix[i] for i in range(0,len(embeddings_matrix))]\n",
    "Y = tsne.fit_transform(vectors[:1000])\n",
    "plt.figure(figsize=(9,6)) \n",
    "plt.scatter(Y[:, 0], Y[:, 1])\n",
    "\n",
    "for label, x, y in zip(words, Y[:, 0], Y[:, 1]):\n",
    "    plt.annotate(label, xy=(x, y), xytext=(5, 2), textcoords=\"offset points\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(186, 186)"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(words),len(vectors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_2 (Embedding)      (None, 4, 8)              2184      \n",
      "_________________________________________________________________\n",
      "flatten_2 (Flatten)          (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 33        \n",
      "=================================================================\n",
      "Total params: 2,217\n",
      "Trainable params: 2,217\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Embedding(vocab_size, 8, input_length=max_length))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "# compile the model\n",
    "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "# summarize the model\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K-Means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "input: `embeddings_matrix`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* method1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clustering the training sentences with K-means technique\n",
    "from sklearn.cluster import KMeans\n",
    "wcss=[]\n",
    "#k = 6\n",
    "for k in range(1,100):\n",
    "    modelkmeans = KMeans(n_clusters=k, init='k-means++', n_init=10, random_state=0, verbose=False)\n",
    "    modelkmeans.fit(embeddings_matrix)\n",
    "    wcss.append(modelkmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,100),wcss)\n",
    "plt.title('elbow method')\n",
    "plt.xlabel('num of clusters')\n",
    "plt.ylabel('wcss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* method 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(186, 50)"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embeddings_matrix.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "186"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(topic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://ai.intelligentonlinetools.com/ml/k-means-clustering-example-word2vec/\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn import metrics\n",
    "\n",
    "k=3  # modify\n",
    "kmeans = KMeans(n_clusters=k, init='k-means++', n_init=10, random_state=0, verbose=False)\n",
    "kmeans.fit(embeddings_matrix)\n",
    "\n",
    "labels = kmeans.labels_\n",
    "centroids = kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster id labels for inputted data\n",
      "[2 0 0 2 0 2 2 1 1 0 2 2 0 0 0 1 2 2 2 1 0 0 1 2 0 2 0 2 0 0 1 2 0 0 0 0 0\n",
      " 1 0 0 0 0 2 2 0 0 1 1 0 0 2 1 0 1 2 0 0 2 1 2 0 0 0 2 0 2 0 0 0 2 0 0 1 0\n",
      " 2 0 1 0 2 0 0 0 1 0 2 0 2 0 2 0 2 0 0 2 0 1 0 1 2 0 1 2 1 0 0 1 0 0 0 0 2\n",
      " 0 0 0 0 0 2 0 2 0 2 0 0 0 1 0 2 0 2 2 2 0 0 1 1 2 0 0 2 0 1 0 0 2 0 0 1 2\n",
      " 0 1 2 2 2 0 0 0 0 2 1 0 1 0 1 0 0 0 0 1 0 2 1 0 0 0 1 1 0 0 0 0 0 0 0 2 2\n",
      " 0]\n",
      "Score (Opposite of the value of X on the K-means objective which is Sum of distances of samples to their closest cluster center):\n",
      "-2606.4155706426945\n",
      "Silhouette_score: \n",
      "0.09119315489451553\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_km = kmeans.fit_predict(embeddings_matrix)\n",
    "plt.scatter(\n",
    "    embeddings_matrix[y_km == 0, 0], embeddings_matrix[y_km == 0, 1],\n",
    "    s=50, c='lightgreen',\n",
    "    marker='s', edgecolor='black',\n",
    "    label='cluster 1'\n",
    ")\n",
    "\n",
    "plt.scatter(\n",
    "    embeddings_matrix[y_km == 1, 0], embeddings_matrix[y_km == 1, 1],\n",
    "    s=50, c='orange',\n",
    "    marker='o', edgecolor='black',\n",
    "    label='cluster 2'\n",
    ")\n",
    "\n",
    "plt.scatter(\n",
    "    embeddings_matrix[y_km == 2, 0], embeddings_matrix[y_km == 2, 1],\n",
    "    s=50, c='lightblue',\n",
    "    marker='v', edgecolor='black',\n",
    "    label='cluster 3'\n",
    ")\n",
    "\n",
    "# plot the centroids\n",
    "plt.scatter(\n",
    "    kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1],\n",
    "    s=250, marker='*',\n",
    "    c='red', edgecolor='black',\n",
    "    label='centroids'\n",
    ")\n",
    "plt.legend(scatterpoints=1)\n",
    "plt.grid()\n",
    "plt.show()\n",
    "plt.savefig('clustering.png')\n",
    "print (\"Cluster id labels for inputted data\")\n",
    "print (labels)\n",
    "#print (\"Centroids data\")\n",
    "#print (centroids)\n",
    " \n",
    "print (\"Score (Opposite of the value of X on the K-means objective which is Sum of distances of samples to their closest cluster center):\")\n",
    "print (kmeans.score(embeddings_matrix))\n",
    " \n",
    "silhouette_score = metrics.silhouette_score(embeddings_matrix, labels, metric='euclidean')\n",
    " \n",
    "print (\"Silhouette_score: \") # range from -1 to 1\n",
    "print (silhouette_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "label2 = {}\n",
    "for i in range(0,len(topic)):\n",
    "    label2[topic[i]] = kmeans.labels_[i]\n",
    "l1=[]\n",
    "l2=[]\n",
    "l3=[]\n",
    "for label in label2:\n",
    "    if label2[label] == 0:\n",
    "        l1.append(label)\n",
    "    elif label2[label] == 1:\n",
    "        l2.append(label)\n",
    "    elif label2[label] == 2:\n",
    "        l3.append(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103, 33, 50)"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(l1),len(l2),len(l3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Label2 category:**\n",
    "* cluster 1: pop culture\n",
    "* cluster 2: geography\n",
    "* cluster 3: nature\n",
    "\n",
    "**Label2 Error Analysis:**\n",
    "* lots of abbreviation\n",
    "* average word vectors if the topic is a phrase"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Questions:\n",
    "\n",
    "1. how to evaluate result of k-means? (Silhouette)\n",
    "2. how to choose k?\n",
    "3. do we need to human annotate label2?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Next Steps:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. According to k, get datasets with the same label2\n",
    "2. Splitting into Training and Test Data for each dataset\n",
    "3. LSTM, log reg(baseline), transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>cat</th>\n",
       "      <th>topic</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>SeaWorld’s profits fell by 84% and customers a...</td>\n",
       "      <td>ele</td>\n",
       "      <td>SeaWorld</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>The company teaches dolphins and killer whales...</td>\n",
       "      <td>ele</td>\n",
       "      <td>SeaWorld</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>It says fewer people are going to its parks an...</td>\n",
       "      <td>ele</td>\n",
       "      <td>SeaWorld</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>SeaWorld has been in the news since the 2013 d...</td>\n",
       "      <td>ele</td>\n",
       "      <td>SeaWorld</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Animal rights organizations say that orcas kep...</td>\n",
       "      <td>ele</td>\n",
       "      <td>SeaWorld</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  cat     topic  cluster\n",
       "0  SeaWorld’s profits fell by 84% and customers a...  ele  SeaWorld      2.0\n",
       "1  The company teaches dolphins and killer whales...  ele  SeaWorld      2.0\n",
       "2  It says fewer people are going to its parks an...  ele  SeaWorld      2.0\n",
       "3  SeaWorld has been in the news since the 2013 d...  ele  SeaWorld      2.0\n",
       "4  Animal rights organizations say that orcas kep...  ele  SeaWorld      2.0"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = pd.read_csv('dataset.csv',encoding='utf-8')\n",
    "new_df.head()"
   ]
  }
 ],
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